Evaluation apparatus, information processing apparatus, program, film forming system, and article manufacturing method

Abstract

To provide a technique advantageous in obtaining, in detail, the position and the shape of an abnormality of a composition on a substrate which is obtained by a film forming process.SOLUTION: An evaluation apparatus includes an obtaining unit configured to obtain an image of an evaluation region including a film forming region on which a film is formed by a film forming process, and a processor configured to process the image for evaluation. The processor is configured to output a feature concerning an abnormality in the image in accordance with a learned model. The image and design information representing a geometrical feature of the film forming region are input to the learned model.SELECTED DRAWING: Figure 10

Description

[Technical field]

[0001] The present invention relates to an evaluation apparatus, an information processing apparatus, a program, a film forming system, and an article manufacturing method. [Background technology]

[0002] Imprint technology, which is a technology for forming fine patterns, is being put into practical use. One imprint technology is the photocuring method. An imprint device that employs the photocuring method irradiates light to harden a photocurable moldable material (imprint material) supplied on a substrate, with an original plate (mold) in contact with the material. Thereafter, a pattern is formed on the substrate by separating the mold from the hardened imprint material. For example, a device that applies step-and-flash imprint lithography is effective for manufacturing semiconductor devices and the like (Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-80047 A Summary of the Invention [Problem to be solved by the invention]

[0004] When forming a pattern on a substrate using imprinting technology, which is one of the film formation technologies, there are cases where the imprinting material is supplied in an excessive amount, causing it to spill outside the pattern area (seepage). Conversely, there are cases where the imprinting material is supplied in an insufficient amount, causing the imprinting material to not spread thoroughly and resulting in partial failure to form a pattern (underfilling). When seepage occurs, not only will the seeped area result in poor pattern formation, but it may also cause the pattern of the mold that comes into contact with that area to be destroyed. Furthermore, when underfilling occurs, no pattern will be formed in that area, resulting in a defective semiconductor device.

[0005] Therefore, after the imprinting process, it is necessary to detect the presence or absence of such seepage and non-filling, and adjust the supply amount and position of the imprinting material according to the detection result to prevent defects. However, since seepage and non-filling occur in a small area, it is necessary to check a huge number of observation images obtained by a high-magnification microscope with a narrow detection range, which is difficult for a human to do. Therefore, there is a demand for a technology that can inspect the seepage and non-filling from the observation images without human intervention and determine the pattern formation defect caused by the seepage and non-filling. Hereinafter, the pattern formation defect caused by the seepage and non-filling is also referred to as "abnormality".

[0006] The way in which these anomalies appear varies depending on the imprinting conditions. Adjusting the imprinting conditions, including the amount of imprinting material supplied, requires not only detecting the presence or absence of anomalies, but also detailed detection of their position, shape, and other information.

[0007] The present invention provides an advantageous technique for determining in detail the position and shape of an abnormality in a composition on a substrate obtained by a film formation process. [Means for solving the problem]

[0008] According to one aspect of the present invention, there is provided an evaluation device for evaluating a film on a substrate that has undergone a film formation process in which a mold is used to form a film of a composition on a film formation region of the substrate, the evaluation device having an acquisition unit that acquires an image of an evaluation region including the film formation region on which a film has been formed by the film formation process, and a processing unit that processes the image for the evaluation, wherein the processing unit is configured to output features relating to abnormalities in the image according to a learning model, and the image and design information indicating geometric features of the film formation region are input into the learning model. Effect of the Invention

[0009] According to the present invention, it is possible to provide a technique that is advantageous for determining in detail the position and shape of an abnormality in a composition on a substrate obtained by a film formation process. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing the configuration of an imprint apparatus. [Diagram 2] FIG. 2 is a diagram showing the configuration of a wide-area alignment measuring instrument. [Diagram 3] FIG. 1 is a diagram showing a configuration of an article manufacturing system. [Figure 4] 4 is a flowchart showing the operation of the imprint apparatus. [Diagram 5] FIG. 1 illustrates seepage and underfilling. [Figure 6] FIG. 13 illustrates an image including seepage and non-filling. [Figure 7] FIG. 13 is a diagram showing an example of a non-filling that occurs at a mark position. [Figure 8] 13A and 13B are diagrams showing examples of images in which the boundaries of shot areas and the original shapes of marks are unknown. [Figure 9] FIG. 4 is a diagram illustrating an example of design information corresponding to an image obtained by imaging. [Figure 10] FIG. 1 is a diagram showing an example of information input to a machine learning model and information output from the machine learning model. [Figure 11] 1 is a flow chart illustrating a method for detecting anomalies in an image. [Figure 12] Schematic representation of the method carried out during training and testing. [Figure 13] 1 is a flowchart of a method for creating a learning model. [Figure 14] FIG. 1 is a diagram showing the configuration of an evaluation device. [Figure 15] 1A to 1C are diagrams illustrating a method for manufacturing an article according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, the embodiments will be described in detail with reference to the attached drawings. Note that the following embodiments do not limit the invention according to the claims. Although the embodiments describe a number of features, not all of these features are essential to the invention, and the features may be combined in any manner. Furthermore, in the attached drawings, the same reference numbers are used for the same or similar configurations, and duplicated descriptions are omitted.

[0012] The following embodiment relates to a film formation system including a film formation apparatus. The film formation apparatus is used in the manufacture of devices such as semiconductor devices as articles, and places an uncured composition on a substrate, molds the placed composition in a mold, and forms a film of the composition on the substrate. The film formation apparatus may be called a molding apparatus, and similarly, the film formation process may be called a molding process.

[0013] The film formation process includes a contacting step of contacting the moldable material supplied on the substrate with a mold (master plate, template). This contact causes the moldable material to be molded. The film formation process may further include a curing step of curing the moldable material while the moldable material is in contact with the mold. This forms a composition consisting of a cured product of the moldable material on the substrate. The film formation process may further include a separation step of separating the composition consisting of the cured product of the moldable material from the mold.

[0014] The film forming apparatus can be used as an imprinting apparatus that transfers a pattern of a mold to an imprinting material, which is a formable material supplied onto a shot area on a substrate where a pattern is to be formed, by contacting the imprinting material with a pattern portion of a mold. The pattern can be, for example, a semiconductor device pattern (device pattern). In the imprinting apparatus, imprinting can be performed for each of a plurality of shot areas formed on the substrate. Alternatively, the imprinting apparatus can be configured to imprint (i.e., contact) a plurality of shot areas of the substrate collectively (over the entire substrate, or in units of one or more rows of shot areas).

[0015] Alternatively, the film forming apparatus can be used as a planarizing apparatus that performs a planarizing process to form a planarized film of moldable material on a substrate by contacting the moldable material on the substrate with a member having a flat surface (the flat surface of a mold).

[0016] In the following, in order to provide a specific example, a system including an imprint apparatus, which is an example of a film forming apparatus, will be described.

[0017] 1(a) shows a schematic configuration of an imprint apparatus IMP in an embodiment. The imprint apparatus IMP performs an imprint process in which an imprint material IM on a substrate S is brought into contact with a pattern region MP of a mold M, the imprint material IM is cured, and the cured product of the imprint material IM is separated from the mold M. A pattern made of the cured product of the imprint material IM is formed on the substrate S by this imprint process.

[0018] As the imprint material, a curable composition (sometimes called an uncured resin) that is cured by applying energy for curing is used. As the energy for curing, electromagnetic waves, heat, etc. can be used. The electromagnetic waves can be, for example, light having a wavelength selected from the range of 10 nm to 1 mm, such as infrared rays, visible light, and ultraviolet rays. The curable composition can be a composition that is cured by irradiation with light or by heating. Among these, the photocurable composition that is cured by irradiation with light contains at least a polymerizable compound and a photopolymerization initiator, and may further contain a non-polymerizable compound or a solvent as necessary. The non-polymerizable compound is at least one selected from the group consisting of a sensitizer, a hydrogen donor, an internal mold release agent, a surfactant, an antioxidant, and a polymer component. The imprint material can be arranged on the substrate in the form of droplets, or in the form of islands or a film formed by connecting a plurality of droplets. The viscosity of the imprint material (at 25° C.) can be, for example, 1 mPa·s to 100 mPa·s. Examples of the material of the substrate include glass, ceramics, metals, semiconductors, and resins. If necessary, a member made of a material different from that of the substrate may be provided on the surface of the substrate. The substrate may be, for example, a silicon wafer, a compound semiconductor wafer, or quartz glass.

[0019] In this specification and the accompanying drawings, directions are shown in an XYZ coordinate system in which the direction parallel to the surface of the substrate S is the XY plane. The directions parallel to the X-axis, Y-axis, and Z-axis in the XYZ coordinate system are the X direction, Y direction, and Z direction, respectively, and the rotation around the X-axis, the Y axis, and the Z axis are θX, θY, and θZ, respectively. Control or drive regarding the X-axis, Y-axis, and Z-axis means control or drive regarding the direction parallel to the X-axis, the direction parallel to the Y axis, and the direction parallel to the Z axis, respectively. Furthermore, control or drive regarding the θX-axis, θY-axis, and θZ-axis means control or drive regarding the rotation around an axis parallel to the X-axis, the rotation around an axis parallel to the Y axis, and the rotation around an axis parallel to the Z axis, respectively. Furthermore, the position is information that can be specified based on the coordinates of the X-axis, Y-axis, and Z-axis, and the orientation is information that can be specified by the values ​​of the θX-axis, θY-axis, and θZ-axis. Positioning means controlling the position and / or orientation. Alignment may include control of the position and / or orientation of at least one of the substrate and mold.

[0020] The imprint apparatus IMP may include a substrate holding unit 102 that holds a substrate S, a substrate driving mechanism 105 that drives the substrate S by driving the substrate holding unit 102, a base 104 that supports the substrate holding unit 102, and a position measurement unit 103 that measures the position of the substrate holding unit 102. The substrate driving mechanism 105 may include, for example, a motor such as a linear motor.

[0021] The imprint apparatus IMP may include a mold holding part 121 that holds a mold M, a mold driving mechanism 122 that drives the mold M by driving the mold holding part 121, and a support structure 130 that supports the mold driving mechanism 122. The mold driving mechanism 122 may include, for example, a motor such as a voice coil motor.

[0022] The substrate driving mechanism 105 and the mold driving mechanism 122 constitute a driving mechanism for adjusting the relative position and the relative attitude of the substrate S and the mold M. The adjustment of the relative position of the substrate S and the mold M by the driving mechanism includes driving for contact of the mold with the imprint material on the substrate S and separation of the mold from the hardened imprint material (pattern of the hardened material). The substrate driving mechanism 105 can be configured to drive the substrate S about a plurality of axes (e.g., three axes of the X axis, the Y axis, and the θZ axis, preferably six axes of the X axis, the Y axis, the Z axis, the θX axis, the θY axis, and the θZ axis). The mold driving mechanism 122 can be configured to drive the mold M about a plurality of axes (e.g., three axes of the Z axis, the θX axis, and the θY axis, preferably six axes of the X axis, the Y axis, the Z axis, the θX axis, the θY axis, and the θZ axis).

[0023] The imprint apparatus IMP may include a mold transport mechanism 140 that transports the mold M, and a mold cleaner 150. The mold transport mechanism 140 may be configured to, for example, transport the mold M to the mold holding unit 121, or transport the mold M from the mold holding unit 121 to an original plate stocker (not shown) or the mold cleaner 150. The mold cleaner 150 cleans the mold M with ultraviolet light, a chemical solution, or the like.

[0024] The mold holding part 121 may include a window member 125 that forms a pressure-controlled space CS on the back surface (the surface opposite to the pattern area MP in which the pattern to be transferred to the substrate S is formed) of the mold M. The imprint apparatus IMP may include a deformation mechanism 123 that controls the pressure in the pressure-controlled space CS (hereinafter referred to as cavity pressure) to deform the pattern area MP of the mold M into a convex shape toward the substrate S, as shown typically in FIG. 1(b).

[0025] The imprint apparatus IMP may include an alignment measurement instrument 106, a wide-angle alignment measurement instrument 151, a curing unit 107, an imaging unit 112, and an optical member 111. The alignment measurement instrument 106 measures the relative position between the alignment marks of the substrate S and the mold M by illuminating the marks and capturing an image of the marks. The alignment measurement instrument 106 may be positioned by a driving mechanism (not shown) according to the position of the alignment mark to be observed. The wide-angle alignment measurement instrument 151 is a measurement instrument having a wider field of view than the alignment measurement instrument 106, and measures the position of the substrate S by illuminating the alignment mark of the substrate S and capturing an image of the marks. By measuring the position of the substrate S with the wide-angle alignment measurement instrument, the alignment mark of the substrate S can be moved within the field of view of the alignment measurement instrument 106.

[0026] The curing unit 107 irradiates the imprint material IM with energy for curing the imprint material IM (for example, light such as ultraviolet light) via the optical member 111, thereby curing the imprint material IM. The imaging unit 112 captures images of the substrate S, the mold M, and the imprint material IM via the optical member 111 and the window member 125.

[0027] The wide-angle alignment measuring instrument 151 may have a mechanism for switching the wavelength of the illumination light. For example, the wide-angle alignment measuring instrument 151 has a wavelength filter arranged on the optical path and has a mechanism for switching the wavelength filter. Alternatively, the wide-angle alignment measuring instrument 151 may have a configuration capable of simultaneously capturing images of multiple wavelengths, as shown in FIG. 2. The measuring instrument in FIG. 2 includes a light source LS, multiple half mirrors 162 branching off from the optical path, multiple wavelength filters 163 each transmitting different wavelengths, and multiple image sensors 164, and can capture images of different wavelengths simultaneously. The wide-angle alignment measuring instrument 151 may also have a mechanism for switching the amount of light of the illumination light. For example, the wide-angle alignment measuring instrument 151 may have a mechanism for switching the ND filter arranged on the optical path. The wide-angle alignment measuring instrument 151 may also have multiple optical systems, such as a bright-field optical system and a dark-field optical system, and have a mechanism for switching the optical system through which the image to be captured passes. Furthermore, the wide-angle alignment measurement instrument 151 may have a mechanism for switching the polarization of the illumination light or the received light. For example, the wide-angle alignment measurement instrument 151 may also have a mechanism for switching a polarizing filter disposed on the optical path.

[0028] The imprint apparatus IMP may include a dispenser 108 that disposes the imprint material IM on the substrate S. The dispenser 108, for example, dispenses the imprint material IM so that the imprint material IM is disposed on the substrate S according to a drop recipe that indicates the placement of the imprint material IM. The imprint apparatus IMP may include a control unit 110 that controls the substrate driving mechanism 105, the mold driving mechanism 122, the deformation mechanism 123, the mold transport mechanism 140, the mold cleaner 150, the alignment measuring instrument 106, the curing unit 107, the imaging unit 112, the dispenser 108, and the like. The control unit 110 may be configured, for example, by a PLD (abbreviation for Programmable Logic Device) such as an FPGA (abbreviation for Field Programmable Gate Array), an ASIC (abbreviation for Application Specific Integrated Circuit), a general-purpose computer with a program built in, or a combination of all or part of these.

[0029] 3 illustrates an example of the configuration of an article manufacturing system 401 for manufacturing an article such as a semiconductor device. The article manufacturing system 401 may include, for example, one or more imprint apparatuses IMP and one or more inspection apparatuses 405 (for example, an overlay inspection apparatus, a CD inspection apparatus, a defect inspection apparatus, an electrical property inspection apparatus). The article manufacturing system 401 may also include one or more substrate processing apparatuses 406 (etching apparatus, film formation apparatus). The article manufacturing system 401 may also include an evaluation apparatus 407, which will be described later. These apparatuses may be connected to a control apparatus 403, which is an external apparatus different from the imprint apparatus IMP, via a network 402, and may be controlled by the control apparatus 403.

[0030] The evaluation device 407 may be configured by an information processing device. The information processing device may be configured by, for example, a PLD (abbreviation of Programmable Logic Device) such as an FPGA (abbreviation of Field Programmable Gate Array), an ASIC (abbreviation of Application Specific Integrated Circuit), a general-purpose computer with a built-in program, or a combination of all or part of these.

[0031] FIG. 14 shows a configuration example of the evaluation device 407. The evaluation device 407 may include a control unit 4071, a RAM 4072 that stores temporary data and provides a work area for the control unit 4071, and a ROM 4073 that stores fixed data and programs. The evaluation device 407 may further include a storage device 4072, a display device 4076, and an input device 4075. The storage device 4072 stores a program 4074a for executing the evaluation method according to this embodiment. The network I / F 4077 is an interface for connecting to the network 402. In this embodiment, the network I / F 4077 may function as an acquisition unit that acquires an image including a composition formed on a substrate by a film formation process. In addition, the control unit 4071 may function as a processing unit that processes the acquired image for evaluation. In addition, the control unit 4071 may also function as a display control unit that controls the display of the display unit 4076.

[0032] The function of the evaluation apparatus 407 may be realized by any one or a combination of the control unit 110 of the imprint apparatus IMP, the control device 403, or a control unit of the inspection apparatus 405. In this embodiment, a system including the imprint apparatus IMP and the evaluation apparatus 407 may be understood as a film formation system or a lithography system.

[0033] The lithography method according to this embodiment will be described below. In this embodiment, after performing an imprint process, an image of a shot area (film formation area) where a pattern is to be formed and an area (evaluation area) including the periphery thereof is acquired by imaging. This image is used to detect seepage and non-filling. Machine learning is used to detect seepage and non-filling. In machine learning, an object detection algorithm can be used to detect anomalies.

[0034] The operation of the imprint apparatus IMP will be described with reference to the flowchart of Fig. 4. The operation shown in Fig. 4 can be controlled by the control unit 110.

[0035] In step S101, a substrate S is transported by a substrate transport mechanism (not shown) from a transport source (for example, an intermediate section between a pre-processing device and an imprint device IMP) onto the substrate holding part 102. A wide-angle alignment measuring instrument 151 is used to observe a mark on the substrate S to measure the position of the transported substrate S on the substrate holding part 102. The control part 110 positions the substrate S based on the position obtained by the measurement.

[0036] In steps S102 to S106, an imprint process (pattern formation) on a shot area selected from among the multiple shot areas of the substrate S is performed.

[0037] In step S102, the imprint material IM is placed on the selected shot area by the dispenser 108. This process can be performed by discharging the imprint material IM from the dispenser 108 while the substrate S is driven by the substrate driving mechanism 105.

[0038] In step S103, the substrate S and the mold M are driven relatively by at least one of the mold driving mechanism 122 and the substrate driving mechanism 105 so that the pattern region MP of the mold M comes into contact with the imprint material IM on the shot region. In one example, the mold M is driven by the mold driving mechanism 122 so that the pattern region MP of the mold M comes into contact with the imprint material IM on the shot region. In the process of bringing the pattern region MP of the mold M into contact with the imprint material IM, the deformation mechanism 123 can deform the pattern region MP of the mold M into a convex shape toward the substrate S.

[0039] In step S104, alignment between the shot area and the pattern area MP of the mold M can be performed. The alignment can be performed by measuring the relative position between the alignment mark of the shot area to be imprinted and the alignment mark of the mold M by the alignment measurement device 106 so that the relative position falls within a tolerance range of the target relative position. In the alignment, the substrate S and the mold M are driven relatively by at least one of the mold driving mechanism 122 and the substrate driving mechanism 105. The target relative position between the alignment mark of the shot area to be imprinted and the alignment mark of the mold M can be determined by a correction value determined from past results of an overlay inspection device or the like.

[0040] In step S105, the curing unit 107 irradiates the imprint material IM between the substrate S and the pattern region MP of the mold M with energy for curing the imprint material IM. This cures the imprint material IM, and a cured product of the imprint material IM is formed.

[0041] In step S106, the substrate S and the mold M are driven relatively by at least one of the mold driving mechanism 122 and the substrate driving mechanism 105 so that the cured product of the imprint material IM and the pattern region MP of the mold M are separated. In one example, the mold M is driven by the mold driving mechanism 122 so that the cured product of the imprint material IM and the pattern region MP of the mold M are separated. When the cured product of the imprint material IM and the pattern region MP of the mold M are separated, the pattern region MP of the mold M can also be deformed into a convex shape toward the substrate S. Furthermore, imaging is performed by the imaging unit 112, and the state of separation between the imprint material IM and the mold M is observed based on the captured image.

[0042] In step S107, the control unit 110 determines whether the imprint processing of steps S102 to S106 has been performed on all shot areas of the substrate S. If the imprint processing of steps S102 to S106 has been performed on all shot areas of the substrate S, the process proceeds to step S108. If an unprocessed shot area exists, the process returns to step S102. In this case, the imprint processing of steps S102 to S106 is performed on a shot area selected from the unprocessed shot areas.

[0043] In step S108, in order to detect anomalies, an image of an area (evaluation area) including a shot area (film formation area) after imprinting is acquired. For example, the control unit 110 uses a wide-angle alignment measuring instrument 151 to capture an image of the shot area and its periphery. When the field of view of the wide-angle alignment measuring instrument 151 is narrow with respect to the shot area, the substrate driving mechanism 105 may be driven to change the position of the substrate S, and multiple images may be captured to obtain an image of a desired area. The image obtained in step S108 may be used as a learning image to be described later. In addition, the image obtained in step S108 may be used as an image for anomaly detection. Here, an example in which an image is captured by the wide-angle alignment measuring instrument 151 will be described, but the present invention is not limited thereto. For example, the image may be captured using the alignment measuring instrument 106 or the imaging unit 112.

[0044] In the above procedure, step S108 is executed after the imprint process is performed on all of the multiple shot areas, but this is not limited to the above. For example, after a pattern is formed in each shot area (after step S106), an image of the imprinted pattern for each shot area may be captured. As will be described later, an image of the pattern on the substrate unloaded from the imprint apparatus may be captured by an apparatus other than the imprint apparatus in a manner similar to step S108.

[0045] In step S109, the substrate S is transported from the substrate holding unit 102 to a destination (for example, an intermediary unit between the imprint apparatus IMP and the post-processing apparatus) by a substrate transport mechanism (not shown). When a lot consisting of multiple substrates is processed, the operation shown in FIG. 4 is performed for each of the multiple substrates.

[0046] Next, an example of an abnormality that occurs during imprinting will be described. Fig. 5 is a side view of a state in which the mold M and the imprint material IM on the substrate S are in contact with each other (after completion of step S103, for example, in steps S104 and S105). "Seeping out" refers to a state in which the imprint material IM protrudes from the contact area between the mold M and the imprint material IM, as shown in Fig. 5(a). "Unfilled" refers to a state in which there are portions between the mold M and the substrate S that are not filled with the imprint material IM, as shown in Fig. 5(b).

[0047] FIG. 6 shows an example of an image obtained by imaging in step S108 when seepage and non-filling have occurred. FIG. 6 shows an image of a state in which a pattern of the imprint material IM has been formed in the shot area by the imprint process. Normally, as shown in FIG. 6(a), the imprint material IM is filled up to the boundary 601 of the shot area to form a pattern. On the other hand, in the case of non-filling, as shown in FIG. 6(b), the imprint material IM does not reach the boundary 601 of the shot area, and the unfilled part is imaged as white (or black). Also, in the case of seepage, as shown in FIG. 6(c), the imprint material IM protrudes from the boundary 601 of the shot area and goes beyond the boundary 601, and is imaged as black (or white).

[0048] FIG. 7 shows an example of an unfilled mark position used for alignment or inspection. Each of FIGS. 7(a) and (b) shows an image IMG of a shot area. The area shown in gray in each image is the shot area of ​​the inspection target filled with the imprint material IM. The shot area includes a first mark 701 and a second mark 702, which are different in shape from each other. In FIG. 7(a), the first mark 701 and the second mark 702 are shown in black, which is a normal state in which the imprint material IM is filled inside the marks. On the other hand, in FIG. 7(b), the first mark 701 and the second mark 702 are partially shown in white, which is a state in which the imprint material IM is not sufficiently filled inside the marks (unfilled area N).

[0049] If the mold M comes into contact with the shot region where seepage has occurred, there is a risk of destroying the pattern formed on the mold M. Furthermore, if an unfilled area occurs, no pattern is formed there, resulting in a defective semiconductor device. For this reason, it is necessary to detect the presence or absence of seepage or unfilled area after the imprint process and adjust the imprint conditions to prevent the above-mentioned defects.

[0050] As an example of an adjustment method, a method of changing the amount of imprint material supplied depending on the amount of seepage or unfilled area that has occurred is considered. To perform this adjustment, it is necessary to obtain information on the position, size, and shape of the area where the amount of imprint material is insufficient or excessive. In this embodiment, this information is obtained (detected) from an image by machine learning. Examples of detection methods include a method that utilizes a model or the like, as exemplified below. -Convolutional Neural Network structure model, -Models with AutoEncoder mechanisms such as U-net, A model based on Region-Convolutional Neural Network (R-CNN). Using methods that utilize these models, the presence or absence of a target object can be calculated for each pixel in an image, and the detailed shape of the object can be determined by labeling it.

[0051] On the other hand, there are problems to be solved in detecting the position, size, and shape of an abnormality from a captured image. Although a line representing the boundary 601 of the shot area is drawn in FIGS. 6(a) to 6(c), such a line does not necessarily exist in reality. Therefore, when an image such as that shown in FIG. 8(a) is obtained by imaging, the boundary position of the shot area cannot be determined from the information of this image alone, and therefore it is not possible to determine the size of the unfilled or seeped area in the imprint material area 801. Furthermore, when images of the mark portions 802 and 803 as shown in FIG. 8(b) are obtained by imaging, it is not possible to determine whether or not they are abnormal from the images of the mark portions 802 and 803 alone unless their original shapes are known.

[0052] To address this problem, in this embodiment, anomalies are detected using design information indicating the geometric characteristics of the shot area. The geometric characteristics of the shot area may include information specifying the boundary position of the shot area, the position and shape of a mark within the shot area, and the like, as described below. Hereinafter, these pieces of information will be referred to as "design information." FIG. 9(a) is an image showing design information corresponding to the position where the image shown in FIG. 6(a) was captured. The image in FIG. 9(a) shows how far the imprint material IM in FIG. 6(a) should be filled. In this embodiment, as shown in FIG. 10(a), two images, the image obtained by capturing the image in FIG. 6(a) and the image showing the design information in FIG. 9(a), are input to the machine learning model as feature quantities. This allows the machine learning model to recognize the boundary of the shot area, and correctly detect the position and size where there is no filling or seepage.

[0053] Moreover, the image in FIG. 9(b) is an image showing the design information (position and shape) of the mark corresponding to the position where the image shown in FIG. 7(a) was captured. The image in FIG. 9(b) shows the exact position and shape of the mark in FIG. 7(a). In this embodiment, as shown in FIG. 10(b), two images, the image obtained by capturing the image in FIG. 6(b) and the image showing the design information in FIG. 9(b), are input to the machine learning model as features. This allows the machine learning model to correctly detect the unfilled area in the mark portion.

[0054] In addition, in semiconductor devices, circuits are usually formed by forming different patterns multiple times on top of each other. Therefore, patterns are already formed on the board, and the image obtained by imaging may contain patterns other than the pattern to be detected. In such cases, the non-detection patterns can be excluded from the design information of the already formed non-detection patterns, thereby enabling efficient and accurate anomaly detection.

[0055] In the above example, the design information is represented in the form of an image, and the image representing the design information is input to the machine learning model. Alternatively, the design information may be represented as information on vertices, lines, and polygons, such as shot boundaries and mark shapes, and the information may be input to the machine learning model.

[0056] Design information, i.e., geometric characteristics of the shot area that specify the boundary position of the shot area, the position and shape of the mark in the shot area, etc., can be acquired, for example, from recipe information input in advance for pattern formation. In addition, the design information may be acquired by performing measurements, using an imaging device or a measuring device, on a substrate that has been correctly imprinted (without abnormalities) under the same conditions as the inspection target.

[0057] In this embodiment, abnormalities in shot edge regions and marks have been described, but abnormalities in patterns other than marks formed on shot regions can also be detected in the same manner.

[0058] The image evaluation method executed by the evaluation device 407 will be described with reference to the flowchart of Fig. 11. In the evaluation method, an abnormality contained in the image acquired in step S108 is detected, and the type of the abnormality (seepage / non-filling) is determined. The program of the evaluation method corresponding to the flowchart of Fig. 7 is stored in, for example, the storage device 4074, and is executed by the control unit 4071 (processing unit) after being loaded into the RAM 4072.

[0059] In S201, the control unit 4071 reads a machine learning model (inference model, hereinafter also simply referred to as "model") that outputs one or more abnormal features in an image. The model is a model that is created in advance from an image acquired under conditions similar to the conditions of the imprint material to be inspected and the measurement conditions of the image acquired in S108. The procedure for creating the model will be described later.

[0060] Thereafter, steps S202 to S205 are repeated to obtain inspection results for each image. In S202, the control unit 4071 reads the image acquired in S108 as an image for inspection. In S203, the control unit 4071 obtains design information of the pattern corresponding to the image read in S202. In S204, the control unit 4071 provides the image read in S202 and the design information acquired in S203 as input to the model read in S201, and obtains the features of the anomaly on the input image as output. The features of the anomaly are obtained for each anomaly on the image. The obtained features of the anomaly may include the type of anomaly (seepage / non-filling), the coordinates of the vertices of a rectangle that encloses the anomaly area, the certainty of the detected anomaly, etc., in addition to the position, size, and shape of the anomaly. Here, the certainty of the detected anomaly is a value that indicates the reliability of the detection result, and is automatically calculated by the model for each detected anomaly. The types of anomalies that can be detected include seepage and non-filling as described above, but other types of anomalies can also be detected by having the model learn.

[0061] In S205, the control unit 4071 performs post-processing on the output from the model obtained in S204. For example, the post-processing may include classifying the anomaly by comparing the certainty of the anomaly with a predetermined threshold for each detected anomaly. For example, when the certainty is expressed as a value between 0 and 1, an anomaly with a certainty of 0.5 or less can be classified as an anomaly that is not subject to detection. Classification conditions such as thresholds when classifying based on the certainty can be changed depending on various data such as the imprint material to be inspected, recipe information, dimming conditions at the time of imaging, and mode at the time of imaging.

[0062] Next, a method for calculating (learning) an inference model (learning model) for anomaly detection by the evaluation device 407 will be described with reference to Figs. 12 and 13. Fig. 12 is a schematic diagram of a method executed during learning and inspection. Fig. 13 is a flowchart of learning for anomaly detection. The evaluation device 407 may include a machine learning unit that generates an inference model by machine learning. The machine learning unit performs machine learning on the relationship between an image of an evaluation area, which is an area including a film formation area, and design information of the film formation area as input, and features related to anomalies as output. A specific description will be given below.

[0063] In step S301, the control unit 4071 acquires a learning image 801 in the same manner as in step S108. Specifically, the control unit 4071 collects an image 801 similar to an image during inspection based on the conditions of the imprint material used when capturing the image during inspection and the measurement conditions. The control unit 4071 also collects learning images 801 in a plurality of shot regions using a plurality of substrates. It is desirable to use a large number of images for learning, and it is assumed that the images include a sufficient amount of samples of the abnormality to be detected. The control unit 4071 also acquires design information 802 corresponding to the learning image 801 in the same manner as in S203.

[0064] In step S302, feature information is created that indicates features related to anomalies corresponding to each image acquired in step S301. Specifically, each acquired image is visually inspected, and feature information 803 including information on the category, size, position, etc. of all anomalies present in each image is created.

[0065] In step S303, the control unit 4071 performs machine learning on the relationship between the image and design information acquired in S301 and the anomaly feature information 803 created in S302, to create a learning model. Here, optimization is performed, for example, by using the image 801 and design information 802 as input data for a neural network created in advance, and the anomaly feature information 803 as output (teacher). Through this optimization, a learning model (neural network) 804 is created.

[0066] In step S304, the control unit 4071 stores the created learning model 804 in the memory unit 805. In the above example, the evaluation device 407 is described as an information processing device that performs both learning and inspection, but the information processing device that performs learning and the information processing device that performs inspection may be configured separately. In that case, the first information processing device creates a learning model and transfers the learning model to the second information processing device that performs inspection. The second information processing device uses the learning model transferred from the first information processing device to inspect the input image.

[0067] According to the embodiment described above, it is possible to automate the detection of abnormalities in the periphery of a shot area, and to classify the abnormalities in more detail. In the above embodiment, an imprint apparatus has been described. As described above, in the case of an imprint apparatus, the "evaluation area" is an area including the shot area and its periphery. In contrast, when the present invention is applied to a planarization apparatus, the "evaluation area" is expected to be an area including the entire substrate.

[0068] <Embodiment of the article manufacturing method> The article manufacturing method according to the embodiment of the present invention is suitable for manufacturing articles such as microdevices such as semiconductor devices and elements having a microstructure. The article manufacturing method of the present embodiment may include a step of forming a layer of a composition on a substrate by a molding device in a molding system, and a step of processing the substrate on which the layer is formed. Furthermore, such a manufacturing method may include other well-known steps (oxidation, film formation, deposition, doping, planarization, etching, resist stripping, dicing, bonding, packaging, etc.). The article manufacturing method of the present embodiment is advantageous in at least one of the performance, quality, productivity, and production cost of the article compared to conventional methods.

[0069] The pattern of the cured product formed by using the imprinting apparatus is used permanently on at least a part of various articles, or temporarily when manufacturing various articles. The articles include electric circuit elements, optical elements, MEMS, recording elements, sensors, and molds. Examples of the electric circuit elements include volatile or non-volatile semiconductor memories such as DRAM, SRAM, flash memory, and MRAM, and semiconductor elements such as LSI, CCD, image sensors, and FPGA. Examples of the molds include molds for imprinting.

[0070] The pattern of the cured product is used as it is as at least a part of a component of the article, or is used temporarily as a resist mask, which is removed after etching or ion implantation in a substrate processing step.

[0071] Next, a method for manufacturing an article will be described. In step SA of Fig. 15, a substrate 1z such as a silicon substrate having a workpiece 2z such as an insulator formed on its surface is prepared, and then an imprint material 3z is applied to the surface of the workpiece 2z by an inkjet method or the like. Here, a state in which the imprint material 3z in the form of multiple droplets is applied onto the substrate is shown.

[0072] In step SB in Fig. 15, the mold 4z for imprinting is placed facing the imprint material 3z on the substrate with the side on which the concave-convex pattern is formed. In step SC in Fig. 15, the substrate 1z to which the imprint material 3z has been applied is brought into contact with the mold 4z, and pressure is applied. The imprint material 3z fills the gap between the mold 4z and the workpiece 2z. When light is irradiated through the mold 4z in this state as energy for curing, the imprint material 3z is cured.

[0073] 15, after the imprint material 3z is cured, the mold 4z and the substrate 1z are separated, and a pattern of the cured product of the imprint material 3z is formed on the substrate 1z. In this cured product pattern, the recesses of the mold correspond to the protrusions of the cured product, and the protrusions of the mold correspond to the recesses of the cured product, i.e., the recessed and protruding patterns of the mold 4z are transferred to the imprint material 3z.

[0074] In step SE of Fig. 15, etching is performed using the pattern of the cured material as an etching-resistant mask, and the portions of the surface of the workpiece 2z where there is no cured material or where only a thin layer remains are removed to form grooves 5z. In step SF of Fig. 15, the pattern of the cured material is removed to obtain an article in which grooves 5z are formed on the surface of the workpiece 2z. Here, the pattern of the cured material is removed, but it may be used as an interlayer insulating film included in a semiconductor element or the like, that is, a component of an article, without being removed after processing.

[0075] (Other embodiments) The present invention can also be realized by a process in which a program for implementing one or more of the functions of the above-described embodiments is supplied to a system or device via a network or a storage medium, and one or more processors in a computer of the system or device read and execute the program. The present invention can also be realized by a circuit (e.g., ASIC) for implementing one or more of the functions.

[0076] The disclosure of this specification includes at least the following evaluation apparatus, information processing apparatus, program, film forming system, and article manufacturing method. (Item 1) An evaluation apparatus for evaluating a film on a substrate that has been subjected to a film formation process in which a film of a composition is formed on a film formation region of the substrate using a mold, the apparatus comprising: an acquisition unit that acquires an image of an evaluation area including a film-formation area on which a film is formed by the film formation process; a processing unit for processing the image for said evaluation; having The processing unit is configured to output features related to anomalies in the image according to a learning model; The image and design information indicating geometric characteristics of the film formation region are input into the learning model. An evaluation device comprising: (Item 2) The evaluation device described in item 1, characterized in that the learning model is a model obtained by machine learning regarding the relationship between, as input, an image of an evaluation area including a membrane formation area and design information of the membrane formation area, and, as output, features related to abnormalities. (Item 3) 2. The evaluation device according to item 1, further comprising a machine learning unit that generates the learning model by machine learning. (Item 4) The evaluation device described in item 3, characterized in that the machine learning unit performs machine learning on the relationship between, as input, an image of an evaluation area including a membrane formation area and design information of the membrane formation area, and, as output, features related to abnormalities. (Item 5) 5. The evaluation device according to any one of claims 1 to 4, wherein the learning model calculates a certainty factor representing the reliability of the detected anomaly. (Item 6) 6. The evaluation device according to item 5, wherein the features related to the abnormality include information on the type, position, size, and certainty of the abnormality in the image. (Item 7) 7. The evaluation device according to item 6, wherein the types of abnormalities include overflow of the composition from the film formation region and underfilling of the composition in the film formation region. (Item 8) 8. The evaluation device according to any one of items 1 to 7, wherein the design information includes information on boundary positions of the film formation region. (Item 9) 8. The evaluation device according to any one of items 1 to 7, wherein the design information includes information on the position and shape of a mark portion in the film formation region. (Item 10) 10. The evaluation device according to any one of claims 1 to 9, wherein the design information is represented in the form of an image. (Item 11) 11. The evaluation device according to any one of items 1 to 10, characterized in that the film formation process is an imprint process in which an imprint material, which is the composition supplied onto the film formation region, is brought into contact with a pattern portion of the mold to transfer a pattern of the mold to the imprint material. (Item 12) 11. The evaluation device according to any one of items 1 to 10, wherein the film formation process is a planarization process in which the composition supplied onto the film formation region is brought into contact with a flat surface of the mold to form a planarized film made of the composition on the substrate. (Item 13) 13. A program for causing a computer to function as each part of the evaluation device according to any one of items 1 to 12. (Item 14) an acquisition unit that acquires an image of an evaluation area including a film formation area of ​​a substrate that has been subjected to a film formation process in which a film of a composition is formed on the film formation area of ​​the substrate using a mold, and design information that indicates a geometric feature of the film formation area; a learning unit that performs machine learning on a relationship between the image and the design information and features related to an abnormality in the image, and creates a learning model; 13. An information processing device comprising: (Item 15) A program for causing a computer to function as each part of the information processing device described in item 14. (Item 16) a film forming apparatus for performing a film forming process to form a film of a composition on a substrate using a mold; An evaluation device according to any one of items 1 to 12, A film forming system comprising: (Item 17) forming a film on a substrate by the film forming apparatus in the film forming system according to item 16; processing the substrate on which the film is formed; and manufacturing an article from the processed substrate.

[0077] The invention is not limited to the above-described embodiments, and various modifications and variations are possible without departing from the spirit and scope of the invention. Accordingly, the following claims are appended to apprise the public of the scope of the invention. [Explanation of symbols]

[0078] IMP: imprint device, S: substrate, M: mold, 110: control unit, 102: substrate holder, 105: substrate drive mechanism, 121: mold holder, 122: mold drive mechanism, 407: evaluation device

Claims

1. An evaluation apparatus for evaluating a film on a substrate that has been subjected to a film formation process in which a film of a composition is formed on a film formation region of the substrate using a mold, the apparatus comprising: an acquisition unit that acquires an image of an evaluation area including a film-formation area on which a film is formed by the film formation process; a processing unit for processing the image for said evaluation; having The processing unit is configured to output features related to anomalies in the image according to a learning model; The image and design information indicating geometric characteristics of the film formation region are input into the learning model. An evaluation device comprising:

2. The evaluation device described in claim 1, characterized in that the learning model is a model obtained by machine learning regarding the relationship between, as input, an image of an evaluation area including a film formation area and design information of the film formation area, and, as output, features related to abnormalities.

3. The evaluation device according to claim 1 , further comprising a machine learning unit that generates the learning model by machine learning.

4. The evaluation device described in claim 3, characterized in that the machine learning unit performs machine learning on the relationship between, as input, an image of an evaluation area including a film formation area and design information of the film formation area, and, as output, features related to abnormalities.

5. The evaluation device according to claim 1 , wherein the learning model calculates a certainty factor that indicates the reliability of a detected anomaly.

6. The evaluation device according to claim 5 , wherein the features related to the abnormality include information on the type, position, size, and certainty of the abnormality in the image.

7. 7. The evaluation device according to claim 6, wherein the types of the abnormality include a protrusion of the composition from the film formation region and a lack of the composition in the film formation region.

8. The evaluation device according to claim 1 , wherein the design information includes information on boundary positions of the film formation regions.

9. 2. The evaluation device according to claim 1, wherein the design information includes information on a position and a shape of a mark portion in the film formation region.

10. 2. The evaluation device according to claim 1, wherein the design information is expressed in the form of an image.

11. The evaluation device according to claim 1, wherein the film formation process is an imprint process in which an imprint material, which is the composition supplied onto the film formation region, is brought into contact with a pattern portion of the mold to transfer a pattern of the mold to the imprint material.

12. 2. The evaluation device according to claim 1, wherein the film formation process is a planarization process in which a planarized film made of the composition is formed on the substrate by contacting the composition supplied onto the film formation region with a flat surface of the mold.

13. A program for causing a computer to function as each unit of the evaluation device according to any one of claims 1 to 12.

14. an acquisition unit that acquires an image of an evaluation area including a film formation area of ​​a substrate that has been subjected to a film formation process in which a film of a composition is formed on the film formation area of ​​the substrate using a mold, and design information that indicates a geometric feature of the film formation area; a learning unit that performs machine learning on a relationship between the image and the design information and features related to an abnormality in the image, and creates a learning model; 13. An information processing device comprising:

15. A program for causing a computer to function as each unit of the information processing device according to claim 14.

16. a film forming apparatus for performing a film forming process to form a film of a composition on a substrate using a mold; An evaluation device according to any one of claims 1 to 12, A film forming system comprising:

17. forming a film on a substrate by the film forming apparatus in the film forming system according to claim 16; processing the substrate on which the film is formed; and manufacturing an article from the processed substrate.

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